RF fingerprinting for location estimation

ABSTRACT

The location of a terminal is estimated within an area of coverage of a wireless network comprising at least one fixed node by measuring a complex frequency response of a radio channel between the terminal and one of said fixed nodes and estimating the location of the terminal on the basis of at least a comparison between data representing a first metric of the measured complex frequency response and data representing a plurality of stored metrics, each of said plurality of stored metrics being related to one of a plurality of different locations within the network and each stored metric being of a complex frequency response measured between the said one of said fixed nodes and the location within the network to which the metric relates.

FIELD OF THE INVENTION

The present invention relates generally to wireless networks, and morespecifically to a method and apparatus for the estimation of thelocation of a terminal within the area of coverage of a wirelessnetwork.

BACKGROUND OF THE INVENTION

Data communications networks often include elements that are connectedby a wireless link. Typically, a number of static or fixed wirelessnodes such as access points may be deployed within a local zone to whichmobile devices may form wireless connections, the connections typicallyconforming to an industry standard such as an IEEE 802.11 standard, forexample IEEE 802.11a. Preferably the wireless access points are arrangedin such a way as to give useful coverage in the zone or area of coverageconcerned and are connected to a data network by wired or wirelesslinks.

There are many applications in which it is necessary to know thelocation of a mobile terminal, and various systems have been proposedand implemented for location estimation of a terminal on the basis ofmeasurements of radio frequency propagation. The known systems can becategorized according to whether or not they require a survey of theradio frequency environment to be carried out.

Systems for location estimation that do not require a survey include,for example, estimation of location by relative time of arrival of radiosignals sent to or from fixed nodes in the network such as access pointsor base stations. The radio signals may be on the uplink or thedownlink, that is to say the times of arrival may be measured at aterminal for signals sent from a plurality of fixed nodes, or at aplurality of fixed nodes for a signal sent from the terminal.Alternatively, it is known to estimate location based on the signalstrength received at fixed nodes from a terminal or at the terminal froma plurality of fixed nodes. Such location systems based on time ofarrival and/or received signal strength to or from a plurality of fixednodes may be referred to as multilateration systems.

It is also known that location may be estimated based on measurements ofangle of arrival of radio signals, which may be referred to astriangulation.

Location systems and methods that do not require a survey typically relyon assumptions about radio frequency propagation, for example that theradio channel follows a direct line of sight path, and that the signalis not attenuated by obstructions. While this may be a reasonableassumption in many environments, indoor environments such as a typicaloffice or business premises may involve severe multi-path, obstructions,and non-line of sight propagation, so that the accuracy of such locationsystems may be severely degraded.

Although satellite navigation systems such as GPS may be employed forlocation purposes outdoors, such systems tend to be less effectiveindoors due to the difficulty in receiving signals transmitted fromsatellites.

To enable location estimation in environments with unpredictable radiopropagation characteristics, typically indoors, it is known to implementa system based on measurements of the radio frequency environment interms of the strength of signals received at a terminal from a pluralityof access points. The location is estimated by comparison of themeasured received signal strengths with a database of received signalstrengths from the access points, previously surveyed at a number ofdifferent locations throughout the area of coverage of the network. Themeasurements of received signal strength represent a “fingerprint” of alocation in the network, that is to say a characteristic measure of theradio frequency environment of the location. However, locationestimation by this method may suffer from ambiguities, in that a similarfingerprint may occur at several locations. The accuracy of the systemmay also be limited.

It is an object of the present invention to provide a method andapparatus which addresses these disadvantages.

SUMMARY OF THE INVENTION

In accordance with a first aspect of the present invention, there isprovided a method of estimation of a location of a terminal within anarea of coverage of a wireless network comprising at least one fixednode, the method comprising:

measuring a complex frequency response of a radio channel between theterminal and one of said fixed nodes; and

estimating the location of the terminal on the basis of at least acomparison between data representing a first metric of the measuredcomplex frequency response and data representing a plurality of storedmetrics,

each of said plurality of stored metrics being related to one of aplurality of different locations within the network and each storedmetric being of a complex frequency response measured between the saidone of said fixed nodes and the location within the network to which themetric relates.

An advantage of estimation of location on the basis of a complexfrequency response is that, since a complex frequency response is avector with multiple components, the measure contains more informationthan, for example, a simple measure of received power. Therefore, themeasure is less likely to experience ambiguities, where the same valuesare experienced at different locations.

An advantage of estimation of location on the basis of a comparisonbetween data representing a first metric data representing a pluralityof stored metrics, each of said plurality of stored metrics beingrelated to one of a plurality of different locations within the networkis that the method is likely to be more accurate than methods that makeassumptions regarding the nature of radio propagation. This isparticularly important in radio environments exhibiting strong multipathor non-line-of-sight propagation, in which the assumptions would belikely to be inaccurate. The stored metrics may be determined by a priorsurvey, for example at points distributed throughout the area ofcoverage of the wireless network, that may be distributed in two orthree dimensions.

Preferably, the first metric and the plurality of stored metrics areautocorrelation functions. The advantage of estimation of location onthe basis of an autocorrelation function of a complex frequency responseis that such an autocorrelation is more slowly changing with locationthan would be the complex frequency response itself. It is thus a robustmeasure, that is tolerant of errors in the locations used to generatestored metrics, and of minor changes in the radio propagationenvironment. Furthermore, the robustness and relatively slow-changingnature is well suited to the process of matching a measured value to astored value, without the need for an unduly small spacing between thelocation of stored metrics.

Conveniently, the first metric and the plurality of stored metrics arefrequency coherence functions. The frequency coherence function alsoexhibits a slowly-changing dependence on location and robustness.

Advantageously, the first metric and the plurality of stored metrics arenormalised frequency coherence functions. An advantage of normalisingthe frequency coherence function is that the metric is made independentof received power, and thus independent of the power of the transmitter,or unknown or uncalibrated gains of antennas etc. This is beneficial inimproving the robustness of the metric to variations in equipment outputpower, so that, for example, a terminal need not be set to the sameoutput power as the equipment used to generate the stored metrics.

Preferably, the measurement of the complex frequency response of theradio channel between the terminal and said one of said fixed nodes isperformed on the basis of a signal transmitted from a selected one ofthe terminal and to the said one of the said fixed nodes, the signalbeing received by the other of the terminal and the said one of the saidfixed nodes. The measurement may thus be performed on the signalreceived by propagating either from the terminal to a fixed node, or inthe opposite direction. Due to the propagation in a radio channel beinggenerally reciprocal, the same complex channel response will be foundirrespective of which direction the propagation takes place, providingthat the carrier frequency is the same.

Advantageously the signal comprises an Orthogonal Frequency DivisionMultiplex symbol. Wireless systems that use Orthogonal FrequencyDivision Multiplex modulation are particularly suited for use ingenerating a complex channel response and a frequency coherencefunction, since received signals are converted to the frequency domainin any case in a standard OFDM receiver, and since the generation of acomplex channel response is also inherent in the standard receptionprocess, using standard pilot tones in the OFDM signal format.

Conveniently, a measure of the difference between the first metric andeach of the stored metrics is evaluated and the position of the terminalis estimated on the basis of the location related to the stored metricfor which the evaluated measure of the difference is a minimum. That isto say that the first metric is used as a fingerprint, that is matchedto a number of stored fingerprints taken at different locations during asurvey. The location with the fingerprint that has the closet match istaken as the most likely location.

Preferably, the fingerprint will comprise a set of metrics relating tothe channel response between the terminal and two or more fixed nodes,and similarly the stored metrics will relate to channel responses to orfrom several fixed nodes. The match between the set of measured metricsand the set of stored metrics can then be done on the basis of a measureof difference, such as the mean square difference, between the measuredset and the stored set. An advantage of using metrics relating to two ormore fixed nodes is that location can be estimated with greater accuracythan would be the case if only a single metric were used.

In accordance with a second aspect of the present invention, there isprovided a method of compiling survey data for use in the estimation ofa location of a terminal within an area of coverage of a wirelessnetwork comprising at least one fixed node, the method comprising:

measuring a complex frequency response of a radio channel between ameasurement node and one of said fixed nodes at a plurality of differentlocations within the network;

deriving data representing a metric of each measured complex frequencyassociate with each of the plurality of different locations; and

storing the derived data for each of said plurality different locationstogether with an indication of the location within the network to whichthe metric relates.

In accordance with a third aspect of the present invention, there isprovided a processor arranged to estimate a location of a terminalwithin an area of coverage of a wireless network comprising at least onefixed node,

wherein the processor is arranged to receive a measurement of a complexfrequency response of a radio channel between the terminal and one ofsaid fixed nodes, and

the processor is arranged to estimate the location of the terminal onthe basis of at least a comparison between data representing a firstmetric of the measured complex frequency response and data representinga plurality of stored metrics,

each of said plurality of stored metrics being related to one of aplurality of different locations within the network and each storedmetric being of a complex frequency response measured between the saidone of said fixed nodes and the location within the network to which themetric relates.

In accordance with a fourth aspect of the present invention, there isprovided a computer readable medium encoded with computer executableinstructions for causing a processor to estimate a location of aterminal within an area of coverage of a wireless network comprising atleast one fixed node by

receiving a measurement of a complex frequency response of a radiochannel between the terminal and one of said fixed nodes, and

estimating the location of the terminal on the basis of at least acomparison between data representing a first metric of the measuredcomplex frequency response and data representing a plurality of storedmetrics,

each of said plurality of stored metrics being related to one of aplurality of different locations within the network and each storedmetric being of a complex frequency response measured between the saidone of said fixed nodes and the location within the network to which themetric relates.

Further features and advantages of the invention will be apparent formthe following description of preferred embodiments of the invention,which are given by way of example only.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram showing a wireless network according to anembodiment of the invention;

FIG. 2 shows the amplitudes of three exemplary complex channelresponses;

FIG. 3 shows the amplitudes of three exemplary frequency coherencefunctions according to an aspect of the invention;

FIG. 4 a is a schematic diagram showing an OFDM symbol as transmitted;

FIG. 4 b is a schematic diagram showing an OFDM symbol as received;

FIG. 4 c is a schematic diagram showing the amplitude of a complexfrequency response of a channel derived from the pilot tones of an OFDMsymbol;

FIG. 5 is a schematic diagram showing a receive chain of an OFDMreceiver adapted to derive a frequency coherence function in anembodiment of the invention;

FIG. 6 is a schematic diagram illustrating a system for the estimationof the location of a terminal on the basis of signals received from theterminal at a fixed node as an embodiment of the invention;

FIG. 7 is a schematic diagram illustrating a system for the estimationof the location of a terminal on the basis of signals received from theterminal at a plurality of fixed nodes as an embodiment of theinvention;

FIG. 8 is a schematic diagram illustrating a system for the estimationof the location of a terminal on the basis of signals received at aterminal from a plurality of fixed nodes as an embodiment of theinvention; and

FIG. 9 shows the steps involved in the estimation of the location of aterminal on the basis of signals received at a terminal from a pluralityof fixed nodes as an embodiment of the invention.

DETAILED DESCRIPTION OF THE INVENTION

In general, the present invention is directed to methods and apparatusfor the estimation of the location of a terminal within an area ofcoverage of a wireless network.

By way of example an embodiment of the invention will now be describedin the context of a WiFi network in a zone such as business premises,which is provided with a network of wireless access points, which mayalso be referred to as connection points or base stations, with whichone or more user equipments can form wireless connections. The accesspoints will typically include transceivers appropriate for a wirelessconnection and also a wired connection to a further portion of a datanetwork, which may be a corporate network including a data centre inanother location or it may include a connection to the internet. Varioustypes of devices equipped with a wireless transceiver can be connectedto the network via the connection points, such as personal computers(PCs) and mobile data units such as PDAs (personal digital assistant),which can be moved within the wireless coverage zone of an access pointand also between access points within the business premises.

The present invention may be applied to data networks used tocommunicate any type of data including but not limited to digitallyencoded voice signals, audio signals generally, images and videostreams. The wireless signals may conform to industry standards such asIEEE 802.11 WiFi, but could also conform to cellular radio standardssuch as LTE or WiMax, or to other industry standards such asultra-wideband radio, or to a proprietary standard, or could conform tono particular accepted standard.

FIG. 1 shows an embodiment of the invention. Three fixed nodes, that maybe access points, 4 a, 4 b and 4 c are shown within the area of coverage2 of a wireless network. Each access point is connected, typically by awired link, to a network 11, that may be a private data network or maybe the public internet. A location estimation processor 12 is alsoconnected to the network 11.

A terminal, 6, is located within the area of coverage 2 of the wirelessnetwork, and is in communication with access points 4 a, 4 b and 4 c.

The embodiment of the invention relates to a method of estimating thelocation of the terminal 6 within the wireless network by the use of aradio frequency (RF) fingerprinting technique. An RF fingerprint is acharacteristic measure of the radio environment. In a preferredembodiment of the invention, the RF fingerprint is a frequency coherencefunction of the complex frequency response or responses of the radiochannel between the terminal and one or more access points. The locationof a terminal is estimated by measuring an RF fingerprint, and bycomparing the measured fingerprint with a stored set of fingerprintsrelating to known locations.

Typically, the stored set of fingerprints has been taken previouslyduring a survey, at locations distributed throughout the area ofcoverage. In FIG. 1, the area of coverage 2 has been previouslysurveyed, by measuring an RF fingerprint at each point on a grid markedas crosses 10 a, 10 b and so on. Typically, grid points may be spaced byapproximately 2 m for a system operating at 2.4 GHz or 5 GHz. Thisfigure is merely an example; greater or lesser values of grid spacingmay also be employed. It is not necessary to cover the entire area ofcoverage in a survey, simply areas of interest, nor is it necessary tofollow a regular grid pattern.

The double ended arrows 8 a, 8 b, and 8 c represent the radio channelsbetween the terminal and the access points. Generally speaking, wirelesslinks are reciprocal, so the complex frequency response of the radiochannels represented by 8 a, 8 b and 8 c could be measured by signalspropagating in either direction. However, in practice there is typicallyan advantage in using the terminal as a transmitter and the accesspoints as receivers, since in this case, as will become apparent, theterminal may be used in unmodified form, and the access points may bemodified to generate the metrics or sets of metrics (i.e. fingerprints)used for location estimation. Generally the terminals may be selectedfrom a variety of legacy items and it is advantageous that theirposition may be found without the need for modification. While theinvention will be described mainly with reference to embodiments inwhich the terminal is a transmitter and the fixed node or nodes are usedas receivers for the purposes of location estimation, it is quitefeasible to perform location estimates with the signal flow in theopposite direction.

As has been mentioned, a frequency coherence function, derived from thecomplex channel response, is used as a characteristic measure of theradio environment in an embodiment of the invention. FIG. 2 shows threecomplex frequency responses (the amplitude only is shown for clarity) 14a, 14 b, 14 c relating to three closely spaced locations (about 10 cmapart). FIG. 3 shows the equivalent frequency coherence functions(again, only the amplitude is shown for clarity) 16 a, 16 b, 16 crelating to the same three locations. It can be seen from a comparisonof the curves that the complex frequency response varies greatly (at agiven frequency) as a result of a small movement, whereas the frequencycoherence function varies less as a result of the small movement. Ingeneral, it has been found that the frequency coherence function is abetter measure to use as a characteristic measure of an RF environmentthan a complex frequency response, because it is more robust in that itrequires little or no averaging to produce repeatable results.

The frequency coherence function (FCF) can be expressed as follows:

$\begin{matrix}{{\hat{R}}_{m} = {\sum\limits_{k = 0}^{N - m - 1}{{\hat{H}}_{k + m}{\hat{H}}_{k}^{*}}}} & (1)\end{matrix}$

Where N is the number of channel response sample points, m is thefrequency offset in terms of sample positions, k is the frequency interms of sample positions, H is the complex channel response, and H“star” is the complex conjugate of the estimated complex channelresponse. The FCF is thus a complex vector of m terms. It can be seenthat the FCF is a form of autocorrelation of the estimated complexchannel response. In practice, the true complex channel response H isnot known, simply the estimated complex channel response. It is commonpractice to refer to an estimated channel response as simply a “channelresponse”, since it is understood that something that is measured isnecessarily an estimate. Similarly, the true frequency coherencefunction R is strictly speaking not known, rather the estimatedfrequency coherence function. The “hat” symbol shown above R and Hdenotes an estimate.

FIGS. 4 a, 4 b, and 4 c illustrate the use of pilot tones in anOrthogonal Frequency Division Multiplex system to derive the complexfrequency response of a channel.

OFDM is the basis of an increasing number of modern wireless standards,amongst them 802.11a, 802.11g, WiMax and LTE. Systems using OFDM areparticularly suited for use in embodiments of the present invention,since they form complex channel response estimate as an inherent part ofthe demodulation process; this could potentially be used to form an FCFwith a relatively minor modification.

FIGS. 4 a, 4 b, and 4 c illustrate the use of pilot tones in anOrthogonal Frequency Division Multiplex system to derive the complexfrequency response of a channel. FIG. 4 a shows an OFDM symbol 18 astransmitted, shown in the frequency domain. It can be seen there are anumber of subcarriers, comprising pilot tones 20 a, 20 b transmitted ata predetermined amplitude and phase and data tones 22 which aremodulated to carry data.

FIG. 4 b shows the OFDM symbol 18 as received, after transmissionthrough a channel. It can be seen that the amplitude profile has beenchanged by the channel response (the phase, not shown, will also beaffected).

FIG. 4 c shows the amplitude of a complex channel response 14 derivedfrom the pilot tone values 24 a, 24 b.

Some transmission formats may contain OFDM symbols in which all of thesubcarriers are transmitted at a known amplitude and phase state, sothat the channel response can be estimated from a received symbol. Suchknown symbols typically occur in a preamble. Other symbols, inparticular symbols carrying payload data, typically have a subset of thesubcarriers reserved as pilot tones (as shown schematically in FIG. 4 a,not representative of actual numbers of pilot tones), and the majorityof the subcarriers used to carry data. These pilot tones are typicallyused to build up a channel estimate over time. An autocorrelation of thechannel estimate, that is to say an autocorrelation of an estimate ofthe complex channel response, can be used as the basis of the FCF. Itcan thus be understood that not every subcarrier position, or point inthe channel estimate, need be derived directly from a pilot symbol, butmay instead be derived by interpolation between pilot symbols.

The following derivation is based on a case in which every subcarrier isa pilot tone of known transmitted state, but from the fore-going it willbe apparent that, although this is a useful example, this need not bethe case.

If S_(k) is the complex amplitude of the signal received at the k-thfrequency at the point (x,y),

D_(k) is the known pilot symbol at the k-th frequency, and

Z_(k) is the noise term at the k-th frequency, then:S _(k)(x,y)=H _(k)(x,y)D _(k) +Z _(k)(x,y)   (2)

And we can estimate the channel as:

$\begin{matrix}{{{\hat{H}}_{k}( {x,y} )} = \frac{S_{k}( {x,y} )}{D_{k}}} & (3)\end{matrix}$

It can be seen that the estimate of H may be used to derive theFrequency Coherence Function using equation (1).

It should also be noted that the frequency coherence function may bederived by a Fourier transform of the power delay profile of a receivedsignal.

FIG. 5 is a schematic diagram showing a receive chain of an OFDMreceiver adapted to derive a frequency coherence function in anembodiment of the invention. It should be noted that the following partsform an entirely conventional OFDM receiver: the low noise amplifier 28,the downconversion 30, the automatic gain control 32, the analogue todigital converter 34, the serial to parallel conversion 36, the cyclicprefix removal 38, the Fast Fourier Transform 40, the parallel to serialconversion 42, the channel correction 44 and the QAM demapping 46.

The channel correction block 44 involves an estimation, that is to saymeasurement, of the complex channel response between the OFDMtransmitter and receiver. The complex channel response is taken from thechannel correction block 44 and passed, according to an embodiment ofthe invention, to an autocorrelation function 48 arranged to produce afrequency coherence function. This may then be power normalised by apower normalisation function 50, to produce a normalised frequencycoherence function.

FIG. 6 shows how a location estimation system may be implemented as anembodiment of the invention, in which a terminal 6 is used astransmitter and an access point is used as a receiver 4. The complexchannel response of the radio channel 8 between the terminal 6 and theaccess point 4 is estimated, that is to say measured, in the complexfrequency response estimation function within the access point 4.

The complex frequency response measurement is then sent via anappropriate link to the location estimation processor 12, which may beimplemented, for example, on a personal computer or as part of a radionetwork controller.

On reception of the complex frequency response, the location estimationprocessor performs an autocorrelation function to produce a measuredfrequency coherence function. The measured frequency coherence functionis then compared with the contents of a database of frequency coherencefunctions, which have typically been previously measured as part of a RFsurvey at a number of different locations within the area of coverage ofthe access point.

The comparison may be on the basis of, for example, the mean squareddifference between corresponding frequency offset components of thefrequency coherence functions. The stored frequency coherence functionwith the minimum mean squared difference is declared the closest matchto the measured frequency coherence function, and the location at whichit is measured is taken as the estimate of the location of the terminal.

It should be noted that the choice of the complex frequency response asthe data to be sent to the location estimation processor was forillustration only; of course, the frequency coherence function could becalculated in the access point, and indeed the location estimationprocessor could be located at the access point.

FIG. 7 shows how a location estimation system may be implemented as anembodiment of the invention in which a terminal 6 is used as transmitterand three access points 4 a, 4 b and 4 c are used as receivers. Thecomplex channel responses of the radio channels 8 a, 8 b and 8 c betweenthe terminal 6 and the access points 4 a, 4 b and 4 c are calculatedwithin the access points 4 a, 4 b and 4 c. The complex frequencyresponse measurements are then sent via an appropriate link to thelocation estimation processor 12, which may be implemented, for example,on a personal computer or as part of a radio network controller. Aspreviously commented upon, the split of functions between physicalentities is simply for illustration, and in principle any of thefunctions following reception of the signal could be carried out in anaccess point, or the location estimation processor, or indeed at adifferent processor or node of the network.

On reception of the complex frequency responses (step S6.01), thelocation estimation processor performs an autocorrelation function oneach complex frequency response (step S6.02) to produce a measuredfrequency coherence function for each, forming a set of frequencycoherence functions, that is to say a set comprising a metrics of eachchannel frequency response. The set may be referred to as an RFfingerprint.

The RF fingerprint, i.e. the set of measured frequency coherencefunctions is then compared (step S6.03) with the contents of a databaseof RF fingerprints, which have typically been previously measured aspart of a RF survey at a number of different locations within the areaof coverage of the access point.

The comparison may be on the basis of, for example, the mean squareddifference between corresponding frequency offset components ofcorresponding frequency coherence functions (i.e frequency coherencefunctions relating to measurements at the same access point). The storedfrequency coherence function with the minimum mean squared difference isdeclared the closest match to the measured frequency coherence function,and the location at which it is measured is taken as the estimate of thelocation of the terminal. This may be referred to as a hard decision.

The location of a terminal may also be estimated by a so-called softdecision, in which the position estimate may take a value between themeasurement points of the survey. In this case, two or more measurementlocations are selected with the least difference, or least mean squareddifference, from the measured fingerprint, and the location is estimatedby interpolation between these points. The advantage over the softdecision may be an improvement in the accuracy of the locationestimation.

FIG. 8 shows an embodiment, in which three access points 4 a, 4 b, 4 care used as a transmitters and a terminal 6 is used as a receiver. Inthis case, it may be convenient for the terminal to receive beaconsignals or frames transmitted by the access points, and to use these asthe basis of the calculation of the frequency coherence functions.However, it should be noted that other transmitted signals could also beused, such as conventional OFDM symbols similarly to the where theterminal is used as a transmitter. Beacon frames are just like any otherframes, in as much as they consist of preambles and OFDM symbols. Theonly substantial difference is that they do not carry payload data, butrather they carry information about the access point, such as itsidentity and capabilities.

FIG. 9 illustrates the steps involved in location estimation using theembodiment of FIG. 7. At step S7.01, beacon signals are received fromeach of the access points 4 a, 4 b, 4 c. At step S7.02, a complexfrequency response is measured for each received beacon signal. At stepS7.03, an autocorrelation is performed on each complex frequencyresponse to produce a set of frequency coherence functions, that is tosay RF fingerprints. The RF fingerprint is then compared with a storedset of RF fingerprints. As before, the RF fingerprint with the bestmatch may be used to indicate the likely location of the terminal as ahard decision, or an interpolation between two or more best matches maybe used to produce an interpolated location estimation as a softdecision.

The above embodiments are to be understood as illustrative examples ofthe invention. It is to be understood that any feature described inrelation to any one embodiment may be used alone, or in combination withother features described, and may also be used in combination with oneor more features of any other of the embodiments, or any combination ofany other of the embodiments. Furthermore, equivalents and modificationsnot described above may also be employed without departing from thescope of the invention, which is defined in the accompanying claims.

1. A method of estimation of a location of a terminal within an area ofcoverage of a wireless network comprising at least one fixed node, themethod comprising: measuring a complex frequency response of a radiochannel between the terminal and one of said fixed nodes; and estimatingthe location of the terminal on the basis of at least a comparisonbetween data representing a first metric of the measured complexfrequency response and data representing a plurality of stored metrics,each of said plurality of stored metrics being related to one of aplurality of different locations within the network and each storedmetric being of a complex frequency response measured between the saidone of said fixed nodes and the location within the network to which themetric relates, wherein the first metric and the plurality of storedmetrics are autocorrelation functions.
 2. A method according to claim 1,wherein the first metric and the plurality of stored metrics arefrequency coherence functions.
 3. A method according to claim 2, whereinthe first metric and the plurality of stored metrics are normalisedfrequency coherence functions.
 4. A method according to claim 1, whereinsaid measuring the complex frequency response of the radio channelbetween the terminal and said one of said fixed nodes is performed onthe basis of a signal transmitted from a selected one of the terminaland the said one of the said fixed nodes, the signal being received bythe other of the terminal and the said one of the said fixed nodes.
 5. Amethod according to claim 4, wherein the signal comprises an OrthogonalFrequency Division Multiplex symbol, wherein said measuring comprises:receiving pilot tone values of the Orthogonal Frequency DivisionMultiplex symbol; estimating the complex frequency response of the radiochannel on the basis of the received pilot tones; and evaluating thefirst metric on the basis of an autocorrelation function of theestimated complex frequency response.
 6. A method according to claim 1,in which said estimating comprises: evaluating a measure of thedifference between the first metric and each of the stored metrics;estimating the position of the terminal on the basis of the locationrelated to the stored metric for which the evaluated measure of thedifference is a minimum.
 7. A method according to claim 1, wherein thewireless network further comprises a plurality of fixed nodes, themethod further comprising; measuring a set of a complex frequencyresponses, the set comprising a complex frequency response for a radiochannel between the terminal and each of two or more of said pluralityof fixed nodes; determining a set of first metrics, each first metriccorresponding to one of the measured set of complex frequency responses;estimating the location of the terminal on the basis of a comparisonbetween each first metric in the set of first metrics and correspondingmetrics in a plurality of stored sets of metrics, each stored set ofmetrics being related to one of a plurality of different locationswithin the network and each set of stored metrics comprising a storedmetric for a complex frequency response measured between each of thesaid two or more of the plurality of fixed nodes and the location withinthe network to which the stored set of metrics relates.
 8. A methodaccording to claim 7, in which said estimating comprises: evaluating ameasure of the difference between each first metric in the set of firstmetrics and the corresponding metric in each of said plurality of storedsets of metrics; calculating a mean squared difference between the setof first metrics and each of the stored sets of metrics, on the basis ofthe evaluated measures; estimating the position of the terminal on thebasis of one of said plurality of locations for which the evaluated meansquared difference is a minimum.
 9. A method according to claim 7, inwhich said estimating comprises: evaluating a measure of the differencebetween each first metric in the set of first metrics and thecorresponding metric in each of said plurality of stored sets ofmetrics; calculating a mean squared difference between the set of firstmetrics and each of the stored sets of metrics, on the basis of theevaluated measures; selecting two or more locations from said pluralityof locations having lower evaluated mean squared difference than theother locations; and estimating the position of the terminal on thebasis of an interpolation between the selected locations.
 10. A methodof compiling survey data for use in the estimation of a location of aterminal within an area of coverage of a wireless network comprising atleast one fixed node, the method comprising: measuring a complexfrequency response of a radio channel between a measurement node and oneof said fixed nodes at a plurality of different locations within thenetwork; and deriving data representing a metric of each measuredcomplex frequency associated with each of the plurality of differentlocations; storing the derived data for each of said plurality differentlocations together with an indication of the location within the networkto which said metric relates, wherein said metric is an autocorrelationfunction.
 11. A method according to claim 10, wherein said metric is afrequency coherence function.
 12. A method according to claim 11,wherein said metric is a normalised frequency coherence function.
 13. Amethod according to claim 10, wherein said measuring the complexfrequency response of the radio channel between the measurement node andsaid one of said fixed nodes is performed on the basis of a signaltransmitted from a selected one of the measurement node and to the saidone of the said fixed nodes, the signal being received by the other ofthe measurement node and the said one of the said fixed nodes.
 14. Aprocessor arranged to estimate a location of a terminal within an areaof coverage of a wireless network comprising at least one fixed node,wherein the processor is arranged to receive a measurement of a complexfrequency response of a radio channel between the terminal and one ofsaid fixed nodes, and the processor is arranged to estimate the locationof the terminal on the basis of at least a comparison between datarepresenting a first metric of the measured complex frequency responseand data representing a plurality of stored metrics, each of saidplurality of stored metrics being related to one of a plurality ofdifferent locations within the network and each stored metric being of acomplex frequency response measured between the said one of said fixednodes and the location within the network to which the metric relates,wherein the first metric and the plurality of stored metrics areautocorrelation functions.
 15. A processor according to claim 14,wherein the first metric and the plurality of stored metrics arefrequency coherence functions.
 16. A processor according to claim 15,wherein the first metric and the plurality of stored metrics arenormalised frequency coherence functions.
 17. A processor according toclaim 14, wherein said measuring the complex frequency response of theradio channel between the terminal and said one of said fixed nodes isperformed on the basis of a signal transmitted from a selected one ofthe terminal and the said one of the said fixed nodes, the signal beingreceived by the other of the terminal and the said one of the said fixednodes.
 18. A non-transitory computer readable medium encoded withcomputer executable instructions for causing a processor to estimate alocation of a terminal within an area of coverage of a wireless networkcomprising at least one fixed node by receiving a measurement of acomplex frequency response of a radio channel between the terminal andone of said fixed nodes, and estimating the location of the terminal onthe basis of at least a comparison between data representing a firstmetric of the measured complex frequency response and data representinga plurality of stored metrics, each of said plurality of stored metricsbeing related to one of a plurality of different locations within thenetwork and each stored metric being of a complex frequency responsemeasured between the said one of said fixed nodes and the locationwithin the network to which the metric relates, wherein the first metricand the plurality of stored metrics are autocorrelation functions.